1. Field of the Invention
The presently claimed and disclosed invention(s) relate to a material property determination system, and an automated method of assigning material properties to image textures within a 3D model. More particularly, but not by way of limitation, the presently claimed and disclosed invention(s) uses an automated methodology to determine and assign material properties to images textures applied to the 3D model by comparing each texture to entries in a palette of material entries and assigning the material palette entry that best matches the one contained in the 3D model image texture.
2. Background of the Art
In the remote sensing/aerial imaging industry, imagery is used to capture views of a geographic area and be able to measure objects and structures within the images as well as to be able to determine geographic locations of points within the image. These are generally referred to as “geo-referenced images” and come in two basic categories:                1. Captured Imagery—these images have the appearance they were captured by the camera or sensor employed.        2. Projected Imagery—these images have been processed and converted such that they conform to a mathematical projection.        
All imagery starts as captured imagery, but as most software cannot geo-reference captured imagery, that imagery is then reprocessed to create the projected imagery. The most common form of projected imagery is the ortho-rectified image. This process aligns the image to an orthogonal or rectilinear grid (composed of rectangles). The input image used to create an ortho-rectified image is a nadir image—that is, an image captured with the camera pointing straight down.
It is often quite desirable to combine multiple images into a larger composite image such that the image covers a larger geographic area on the ground. The most common form of this composite image is the “ortho-mosaic image” which is an image created from a series of overlapping or adjacent nadir images that are mathematically combined into a single ortho-rectified image.
Technology advancements within the computerized three-dimensional modeling industry are providing avenues for physical simulation of real-life and hypothetical situations on computer systems. These models can provide valuable information for strategic and tactical planning. For example, three-dimensional models of city streets can provide first responders information regarding current city developments including entryway locations, building recognition, and the like. This information is valuable in reducing response time during emergency conditions. Further, emergency personal can train for emergency situations through simulated scenarios provided by or with the three dimensional models.
The introduction of metric oblique imagery by Pictometry International Corp has led to the creation of very photo-realistic computerized 3D models by the use of regions within oblique images as textures on the buildings, structures, and objects in the 3D models. This practice not only results in computerized 3D models that are very visually pleasing, but they also contain information about the objects themselves, including clues to the material composition used to construct those objects.
Identifying the material composition is very important when using the 3D models for simulating real-life and hypothetical situations on computer systems, such as blast simulations, weapons penetration, radio wave propagation, signal reflectivity, and other scientific studies where the material composition comes into play in the calculations. Traditionally the properties of these materials have been entered by hand in a very laborious process where an operator selects an individual building or object in the model and then assigns the appropriate building material. Prior to the creation of photo-realistic 3D models from oblique images, this process could even involve field visits to determine the material composition.
It is highly desirable to automate this process, for two primary reasons: speed of production and cost savings. However, to date, an automated method has been elusive because while object or material recognition is a rather easy process for people, it is very difficult for computers. To date, most attempts at automated material classification have concentrated on multi-spectral image collection in hopes that enough color signatures can uniquely identify each material. However, in most cases, multi-spectral data is not available or is limited to only a few color bands and therefore insufficient to differentiate between materials.